About us

Our mission is to reinvent the way people learn, starting with language.

Learning a language can change a life by opening doors to new cultures, careers, and communities. Two billion people around the world are actively trying to learn a language, but the best way to learn (one-on-one tutoring) is hard to access at scale and hasn’t been meaningfully improved in decades. Speak is building a human-level, AI-powered tutor in your pocket: a conversation-first experience that lets learners actually speak, get instant feedback, and progress through carefully designed lessons. The result is a complete path from beginner to confident speaker across multiple languages.

Speak first launched in South Korea in 2019, where Speak has now become the number one language learning app, and we now serve learners across many markets and 15+ languages. Speak is one of the world’s leading AI companies, with over $150m raised in venture investment from OpenAI, Accel, Founders Fund, Khosla Ventures, and more, with a distributed team across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana.

About this role

As a Data Engineer at Speak, you'll play a pivotal role in shaping the future of digital language learning, propelling us towards our mission of making language proficiency accessible to millions worldwide.

Your responsibilities will span the crucial intersection of data infrastructure and analytics, from managing scalable data pipelines, to deploying sophisticated analytics solutions that drive personalized learning experiences. You'll work closely with our product, engineering, and analytics teams to ensure that our platform, powered by cutting-edge technology, is not only robust but also delivers actionable insights that enhance user engagement and learning outcomes.

What you'll be doing

• Design and Build Data Infrastructure: You'll architect and implement robust, scalable data pipelines using Airflow for orchestration and dbt for transformation that ensure efficient data flow and processing. Your work will be critical in managing the ingestion, storage, and accessibility of data from various sources, ensuring our platform's backbone is strong and reliable.

• Enable Data-Driven Decisions: By collaborating with cross-functional teams, you will develop and deploy tools and frameworks that facilitate data access and analysis, empowering product and business teams to make informed decisions. 

• Optimize Data Architecture: Constantly evaluate and refine the data architecture to support our growing data needs and ensure optimal performance. This includes managing a data warehouse and various data sources, as well as implementing best practices for data modeling, data quality, and data governance.

• Support Machine Learning Projects: Work closely with analysts and machine learning engineers by providing them with clean, structured data for building and deploying predictive models that enhance personalized learning experiences and engagement strategies.

• Innovate and Experiment: Stay ahead of the curve by researching and implementing cutting-edge technologies and methodologies in data engineering and analytics.

• Collaborate Across Teams: As a key player in the engineering team, you'll work closely with product managers, analysts, and other engineers to bring data-driven products and features from concept to launch.

What we're looking for

• 5+ years of relevant experience

• Data Modeling: Deep understanding of big data warehouses (BigQuery, Snowflake, Redshift), theories, principles, and practices. Ability to design, implement, and manage data warehouses effectively.

• Programming Skills: Strong programming skills in Python and SQL. Ability to write efficient, reliable, and maintainable code.

• Data Pipeline and ETL Development: Experience in building and optimizing data pipelines, architectures, and datasets. Familiarity with ETL (extract, transform, load) processes and tools.

• Big Data Technologies: Experience with end-to-end data platform beyond creating pipelines, such as data ingestion, reverse ETL, visualization, data observability, etc.

• Cloud Computing: Knowledge of cloud services (GCP, AWS, dbt) and understanding of how to leverage them for data processing and storage solutions.

• Data Analysis and Visualization: Ability to analyze data to identify patterns, anomalies, and insights. Proficiency in using data visualization tools (e.g. Mode) to communicate findings clearly.

• Debugging Skills: Strong problem-solving skills and the ability to approach complex challenges methodically including data inconsistency issues.

• Effective Communication: Ability to communicate technical information to non-techni